Amongst the most generic of techniques for the detection of planar projections of shapes in digital images are the so-called template-matching methods, where a learned image template is compared (matched) against all possible locations in the image. Simple template matching methods operate on luminance values of the template and image and compute a distance metric based on normalised cross-correlation. Normalised cross-correlation is invariant to global and linear illumination changes but not to local non-homogenous changes. It is also not invariant to intra-class variations of a family of objects such as relative luminance values between different regions of the object.
An important issue is that of the property used to match between image and template. The most widely used techniques make use of the image pixel values themselves, and use cross-correlation between a template containing the image pixel values from one or more averaged examples of the shape.
Template matching between binary edge maps of the image and shape has led to faster, and more selective discrimination of shapes than that using simply the pixel values.
European patent application no. EP 1193642 A1 describes a method for recognising a user defined model object within an image. The model object is searched for in an image by generating a match metric using a normalised cross-correlation technique.
It is an object of the invention to provide improved object detection in image processing techniques.